Learning hierarchical poselets for human parsing

  • Wang Y
  • Tran D
  • Liao Z
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We consider the problem of human parsing with part-based models. Most
previous work in part-based models only considers rigid parts (e.g.
torso, head, half limbs) guided by human anatomy. We argue that this
representation of parts is not necessarily appropriate for human
parsing. In this paper, we introduce hierarchical poselets{&}{#}x2013;a
new representation for human parsing. Hierarchical poselets can be
rigid parts, but they can also be parts that cover large portions
of human bodies (e.g. torso {&}{#}x002B; left arm). In the extreme
case, they can be the whole bodies. We develop a structured model
to organize poselets in a hierarchical way and learn the model parameters
in a max-margin framework. We demonstrate the superior performance
of our proposed approach on two datasets with aggressive pose variations.

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  • Yang Wang

  • Duan Tran

  • Zicheng Liao

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